Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Mechanism of damage sensing and cell killing following the inhibition of nucleotide excision repair by fludarabine in quiescent cells

Inhibition of DNA repair by the nucleoside of fludarabine (F-ara-A) induces toxicity in quiescent human cells. The sensing and signaling mechanisms following DNA repair inhibition by F-ara-A are unknown. The central hypothesis of this project was that the mechanistic interaction of a DNA repair initiating agent and a nucleoside analog initiates an apoptotic signal in quiescent cells. The purpose of this research was to identify the sensing and signaling mechanism(s) that respond to DNA repair inhibition by F-ara-A. Lymphocytes were treated with F-ara-A, to accumulate the active triphosphate metabolite and subsequently DNA repair was activated by UV irradiation. Pre-incubation of lymphocytes with 3 μM F-ara-A inhibited DNA repair initiated by 2 J/m2 UV and induced greater than additive apoptosis after 24 h. Blocking the incorporation of F-ara-A nucleotide into repairing DNA using 30 μM aphidicolin considerably lowered the apoptotic response. ^ Wild-type quiescent cells showed a significant loss in viability than did cells lacking functional sensor kinase DNA-PKcs or p53 as measured by colony formation assays. The functional status of ATM did not appear to affect the apoptotic outcome. Immunoprecipitation studies showed an interaction between the catalytic sub-unit of DNA-PK and p53 following DNA repair inhibition. Confocal fluorescence microscopy studies have indicated the localization pattern of p53, DNA-PK and γ-H2AX in the nucleus following DNA damage. Foci formation by γ-H2AX was seen as an early event that is followed by interaction with DNA-PKcs. p53 serine-15 phosphorylation and accumulation were detected 2 h after treatment. Fas/Fas ligand expression increased significantly after repair inhibition and was dependent on the functional status of p53. Blocking the interaction between Fas and Fas ligand by neutralizing antibodies significantly rescued the apoptotic fraction of cells. ^ Collectively, these results suggest that incorporation of the nucleoside analog into repair patches is critical for cytotoxicity and that the DNA damage, while being sensed by DNA-PK, may induce apoptosis by a p53-mediated signaling mechanism. Based on the results, a model is proposed for the sensing of F-ara-A-induced DNA damage that includes γ-H2AX, DNA-PKcs, and p53. Targeting the cellular DNA repair mechanism can be a potential means of producing cytotoxicity in a quiescent population of neoplastic cells. These results also provide mechanistic support for the success of nucleoside analogs with cyclophosphamide or other agents that initiate excision repair processes, in the clinic. ^

Bayesian joint modeling of longitudinal and survival data

The joint modeling of longitudinal and survival data is a new approach to many applications such as HIV, cancer vaccine trials and quality of life studies. There are recent developments of the methodologies with respect to each of the components of the joint model as well as statistical processes that link them together. Among these, second order polynomial random effect models and linear mixed effects models are the most commonly used for the longitudinal trajectory function. In this study, we first relax the parametric constraints for polynomial random effect models by using Dirichlet process priors, then three longitudinal markers rather than only one marker are considered in one joint model. Second, we use a linear mixed effect model for the longitudinal process in a joint model analyzing the three markers. In this research these methods were applied to the Primary Biliary Cirrhosis sequential data, which were collected from a clinical trial of primary biliary cirrhosis (PBC) of the liver. This trial was conducted between 1974 and 1984 at the Mayo Clinic. The effects of three longitudinal markers (1) Total Serum Bilirubin, (2) Serum Albumin and (3) Serum Glutamic-Oxaloacetic transaminase (SGOT) on patients' survival were investigated. Proportion of treatment effect will also be studied using the proposed joint modeling approaches. ^ Based on the results, we conclude that the proposed modeling approaches yield better fit to the data and give less biased parameter estimates for these trajectory functions than previous methods. Model fit is also improved after considering three longitudinal markers instead of one marker only. The results from analysis of proportion of treatment effects from these joint models indicate same conclusion as that from the final model of Fleming and Harrington (1991), which is Bilirubin and Albumin together has stronger impact in predicting patients' survival and as a surrogate endpoints for treatment. ^

Identification of maltreatment type in children with disabilities using the National Child Abuse and Neglect Data System (NCANDS)

This study was a retrospective design and used secondary data from the National Child Abuse and Neglect Data System (NCANDS), provided by the National Data Archive on Child Abuse and Neglect Family Life Development Center administered by Cornell University. The dataset contained information for the year 2005 on children from birth to 18 years of age. Child abuse and neglect for disabled children, was evaluated in-depth in the present study. Descriptive and statistical analysis was performed using the children with and without disabilities. It was found that children with disabilities have a lower rate of substantiation that likely indicates the interference of reporting due to their handicap. The results of this research demonstrate the important need to teach professionals and laypersons alike on how to recognize and substantiate abuse among disabled children.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^